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171 lines
4.4 KiB
171 lines
4.4 KiB
import time
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import json
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import csv
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import os
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import requests
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from bs4 import BeautifulSoup
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import headers
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def download_file(url, local_filename=None):
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if local_filename is None:
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local_filename = url.split('/')[-1]
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print('saving to', local_filename)
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r = requests.get(url, stream=True)
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with open(local_filename, 'wb') as f:
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for chunk in r.iter_content(chunk_size=1024):
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if chunk:
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f.write(chunk)
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return local_filename
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def get_page(company_id, start=0, count=50):
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params = {
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'facet': 'CC',
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'facet.CC': company_id,
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'count': count,
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'start': start,
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}
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response = requests.get('https://www.linkedin.com/sales/search/results', headers=headers.headers, params=params)
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return response.json()
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def get_company(company_id, outname):
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count = 50
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start = 0
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results = get_page(company_id)
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total = results['pagination']['total']
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people = results['searchResults']
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start += count
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while start < total:
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print('getting', start, total)
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time.sleep(1)
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results = get_page(company_id, start)
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people += results['searchResults']
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start += count
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with open(outname, 'w') as outfile:
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json.dump(people, outfile, indent=2)
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return outname
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def get_images(datafile):
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with open(datafile, 'r') as infile:
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people = json.load(infile)
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people = [p['member'] for p in people]
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for p in people:
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if 'vectorImage' not in p:
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continue
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pid = p['memberId']
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outname = 'images/{}.jpg'.format(pid)
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if os.path.exists(outname):
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print('skipping')
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continue
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url = p['vectorImage']['rootUrl']
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url += sorted(p['vectorImage']['artifacts'], key=lambda x: x['width'])[-1]['fileIdentifyingUrlPathSegment']
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print(url)
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download_file(url, outname)
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time.sleep(1)
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def get_profile(pid):
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outname = 'profiles/{}.json'.format(pid)
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if os.path.exists(outname):
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return outname
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out = {}
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url = 'https://www.linkedin.com/sales/people/{},NAME_SEARCH'.format(pid)
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print(url)
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response = requests.get(url, headers=headers.headers)
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soup = BeautifulSoup(response.text, 'html.parser')
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codes = soup.select('code')
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for c in codes:
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try:
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d = json.loads(c.text)
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if 'contactInfo' in d:
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out = d
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break
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except Exception as e:
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continue
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with open(outname, 'w') as outfile:
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json.dump(out, outfile)
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time.sleep(1)
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return outname
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def get_profiles(datafile):
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with open(datafile, 'r') as infile:
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data = json.load(infile)
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for d in data:
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pid = d['member']['profileId']
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get_profile(pid)
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def clean_and_parse(datafile, outname):
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out = []
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with open(datafile, 'r') as infile:
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data = json.load(infile)
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for d in data:
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mid = d['member']['memberId']
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pid = d['member']['profileId']
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imgpath = 'images/{}.jpg'.format(mid)
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if not os.path.exists(imgpath):
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imgpath = None
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item = {
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'name': d['member'].get('formattedName', ''),
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'title': d['member'].get('title', ''),
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'img': imgpath,
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'company': d['company'].get('companyName', ''),
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'location': d['member'].get('location', ''),
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'id': d['member']['memberId'],
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'linkedin': 'https://linkedin.com/in/' + pid,
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}
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# profile_file = 'profiles/{}.json'.format(pid)
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# if os.path.exists(profile_file):
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# with open(profile_file, 'r') as profilein:
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# profile = json.load(profilein)
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out.append(item)
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with open(outname + '.json', 'w') as jsonfile:
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json.dump(out, jsonfile, indent=2)
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with open(outname + '.csv', 'w') as csvfile:
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fieldnames = ['name', 'title', 'location', 'url']
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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for row in out:
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writer.writerow({
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'name': row['name'],
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'title': row['title'],
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'location': row['location'],
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'url': row['linkedin']
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})
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if __name__ == '__main__':
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ICE = '533534'
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datafile = 'ice_raw.json'
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# get_company(ICE, datafile)
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# get_profiles(datafile)
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# get_images(datafile)
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clean_and_parse(datafile, 'ice')
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